Overview

Dataset statistics

Number of variables14
Number of observations105908
Missing cells0
Missing cells (%)0.0%
Duplicate rows263
Duplicate rows (%)0.2%
Total size in memory11.3 MiB
Average record size in memory112.0 B

Variable types

NUM14

Reproduction

Analysis started2020-08-25 01:52:00.659090
Analysis finished2020-08-25 01:52:38.381072
Duration37.72 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 263 (0.2%) duplicate rows Duplicates
V0 has 82464 (77.9%) zeros Zeros
V1 has 11841 (11.2%) zeros Zeros
V2 has 15532 (14.7%) zeros Zeros
V3 has 76247 (72.0%) zeros Zeros
V5 has 49127 (46.4%) zeros Zeros
V6 has 1129 (1.1%) zeros Zeros
V8 has 9520 (9.0%) zeros Zeros
V10 has 12144 (11.5%) zeros Zeros
target has 21359 (20.2%) zeros Zeros

Variables

V0
Real number (ℝ≥0)

ZEROS

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7567889111304151
Minimum0.0
Maximum10.0
Zeros82464
Zeros (%)77.9%
Memory size827.5 KiB
2020-08-25T01:52:38.617627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.957220847
Coefficient of variation (CV)2.58621766
Kurtosis10.64785256
Mean0.7567889111
Median Absolute Deviation (MAD)0
Skewness3.259941101
Sum80150
Variance3.830713445
2020-08-25T01:52:38.724419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
08246477.9%
 
179707.5%
 
247674.5%
 
327832.6%
 
1019241.8%
 
417811.7%
 
512671.2%
 
69200.9%
 
77790.7%
 
86520.6%
 
96010.6%
 
ValueCountFrequency (%) 
08246477.9%
 
179707.5%
 
247674.5%
 
327832.6%
 
417811.7%
 
512671.2%
 
69200.9%
 
77790.7%
 
86520.6%
 
96010.6%
 
ValueCountFrequency (%) 
1019241.8%
 
96010.6%
 
86520.6%
 
77790.7%
 
69200.9%
 
512671.2%
 
417811.7%
 
327832.6%
 
247674.5%
 
179707.5%
 

V1
Real number (ℝ≥0)

ZEROS

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.224515617328247
Minimum0.0
Maximum10.0
Zeros11841
Zeros (%)11.2%
Memory size827.5 KiB
2020-08-25T01:52:38.843762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.910999864
Coefficient of variation (CV)0.6890730507
Kurtosis-0.915051671
Mean4.224515617
Median Absolute Deviation (MAD)2
Skewness0.2843771158
Sum447410
Variance8.47392021
2020-08-25T01:52:38.947531image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
21209511.4%
 
01184111.2%
 
31173111.1%
 
41157710.9%
 
11135110.7%
 
51125510.6%
 
6104279.8%
 
789928.5%
 
869996.6%
 
1058355.5%
 
938053.6%
 
ValueCountFrequency (%) 
01184111.2%
 
11135110.7%
 
21209511.4%
 
31173111.1%
 
41157710.9%
 
51125510.6%
 
6104279.8%
 
789928.5%
 
869996.6%
 
938053.6%
 
ValueCountFrequency (%) 
1058355.5%
 
938053.6%
 
869996.6%
 
789928.5%
 
6104279.8%
 
51125510.6%
 
41157710.9%
 
31173111.1%
 
21209511.4%
 
11135110.7%
 

V2
Real number (ℝ≥0)

ZEROS

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.382756732258186
Minimum0.0
Maximum10.0
Zeros15532
Zeros (%)14.7%
Memory size827.5 KiB
2020-08-25T01:52:39.062621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.090336975
Coefficient of variation (CV)0.7051125954
Kurtosis-1.295707287
Mean4.382756732
Median Absolute Deviation (MAD)3
Skewness0.07479127684
Sum464169
Variance9.550182616
2020-08-25T01:52:39.168655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01553214.7%
 
81132610.7%
 
9103409.8%
 
3103379.8%
 
1102379.7%
 
297989.3%
 
693788.9%
 
491688.7%
 
790918.6%
 
587868.3%
 
1019151.8%
 
ValueCountFrequency (%) 
01553214.7%
 
1102379.7%
 
297989.3%
 
3103379.8%
 
491688.7%
 
587868.3%
 
693788.9%
 
790918.6%
 
81132610.7%
 
9103409.8%
 
ValueCountFrequency (%) 
1019151.8%
 
9103409.8%
 
81132610.7%
 
790918.6%
 
693788.9%
 
587868.3%
 
491688.7%
 
3103379.8%
 
297989.3%
 
1102379.7%
 

V3
Real number (ℝ≥0)

ZEROS

Distinct count9
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8173603504928806
Minimum0.0
Maximum10.0
Zeros76247
Zeros (%)72.0%
Memory size827.5 KiB
2020-08-25T01:52:39.284599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.398135035
Coefficient of variation (CV)1.869819067
Kurtosis1.166397569
Mean1.81736035
Median Absolute Deviation (MAD)0
Skewness1.664817053
Sum192473
Variance11.54732172
2020-08-25T01:52:39.388066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
07624772.0%
 
101190011.2%
 
274487.0%
 
552054.9%
 
722182.1%
 
415101.4%
 
98040.8%
 
64290.4%
 
81470.1%
 
ValueCountFrequency (%) 
07624772.0%
 
274487.0%
 
415101.4%
 
552054.9%
 
64290.4%
 
722182.1%
 
81470.1%
 
98040.8%
 
101190011.2%
 
ValueCountFrequency (%) 
101190011.2%
 
98040.8%
 
81470.1%
 
722182.1%
 
64290.4%
 
552054.9%
 
415101.4%
 
274487.0%
 
07624772.0%
 

V4
Real number (ℝ≥0)

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.804726743966461
Minimum0.0
Maximum10.0
Zeros376
Zeros (%)0.4%
Memory size827.5 KiB
2020-08-25T01:52:39.506729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median7
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.981436682
Coefficient of variation (CV)0.2911853416
Kurtosis-0.3641316493
Mean6.804726744
Median Absolute Deviation (MAD)2
Skewness-0.445608642
Sum720675
Variance3.926091323
2020-08-25T01:52:39.609303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
92053619.4%
 
81763716.7%
 
71761516.6%
 
61648415.6%
 
51320712.5%
 
491018.6%
 
1055335.2%
 
342354.0%
 
28880.8%
 
03760.4%
 
12960.3%
 
ValueCountFrequency (%) 
03760.4%
 
12960.3%
 
28880.8%
 
342354.0%
 
491018.6%
 
51320712.5%
 
61648415.6%
 
71761516.6%
 
81763716.7%
 
92053619.4%
 
ValueCountFrequency (%) 
1055335.2%
 
92053619.4%
 
81763716.7%
 
71761516.6%
 
61648415.6%
 
51320712.5%
 
491018.6%
 
342354.0%
 
28880.8%
 
12960.3%
 

V5
Real number (ℝ≥0)

ZEROS

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7384616837254976
Minimum0.0
Maximum10.0
Zeros49127
Zeros (%)46.4%
Memory size827.5 KiB
2020-08-25T01:52:39.715234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.410721193
Coefficient of variation (CV)1.386697915
Kurtosis1.911485609
Mean1.738461684
Median Absolute Deviation (MAD)1
Skewness1.605411866
Sum184117
Variance5.811576668
2020-08-25T01:52:39.814349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
04912746.4%
 
11776016.8%
 
21159310.9%
 
377477.3%
 
453595.1%
 
538983.7%
 
630202.9%
 
725432.4%
 
820521.9%
 
914231.3%
 
1013861.3%
 
ValueCountFrequency (%) 
04912746.4%
 
11776016.8%
 
21159310.9%
 
377477.3%
 
453595.1%
 
538983.7%
 
630202.9%
 
725432.4%
 
820521.9%
 
914231.3%
 
ValueCountFrequency (%) 
1013861.3%
 
914231.3%
 
820521.9%
 
725432.4%
 
630202.9%
 
538983.7%
 
453595.1%
 
377477.3%
 
21159310.9%
 
11776016.8%
 

V6
Real number (ℝ≥0)

ZEROS

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.091796653699437
Minimum0.0
Maximum10.0
Zeros1129
Zeros (%)1.1%
Memory size827.5 KiB
2020-08-25T01:52:39.923592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median6
Q38
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.026935694
Coefficient of variation (CV)0.3327320016
Kurtosis0.04899455946
Mean6.091796654
Median Absolute Deviation (MAD)1
Skewness-0.470656649
Sum645170
Variance4.108468306
2020-08-25T01:52:40.030308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
61977718.7%
 
71952318.4%
 
81690616.0%
 
51686515.9%
 
41066410.1%
 
987048.2%
 
355855.3%
 
228762.7%
 
1025442.4%
 
113351.3%
 
011291.1%
 
ValueCountFrequency (%) 
011291.1%
 
113351.3%
 
228762.7%
 
355855.3%
 
41066410.1%
 
51686515.9%
 
61977718.7%
 
71952318.4%
 
81690616.0%
 
987048.2%
 
ValueCountFrequency (%) 
1025442.4%
 
987048.2%
 
81690616.0%
 
71952318.4%
 
61977718.7%
 
51686515.9%
 
41066410.1%
 
355855.3%
 
228762.7%
 
113351.3%
 

V7
Real number (ℝ≥0)

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.475724213468293
Minimum0.0
Maximum10.0
Zeros53
Zeros (%)0.1%
Memory size827.5 KiB
2020-08-25T01:52:40.146265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median6
Q36
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.242115025
Coefficient of variation (CV)0.2268403186
Kurtosis0.1197024024
Mean5.475724213
Median Absolute Deviation (MAD)1
Skewness-0.2606739656
Sum579923
Variance1.542849736
2020-08-25T01:52:40.250399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
63335131.5%
 
52944827.8%
 
71731316.3%
 
41565014.8%
 
351794.9%
 
835933.4%
 
29310.9%
 
92670.3%
 
11100.1%
 
0530.1%
 
1013< 0.1%
 
ValueCountFrequency (%) 
0530.1%
 
11100.1%
 
29310.9%
 
351794.9%
 
41565014.8%
 
52944827.8%
 
63335131.5%
 
71731316.3%
 
835933.4%
 
92670.3%
 
ValueCountFrequency (%) 
1013< 0.1%
 
92670.3%
 
835933.4%
 
71731316.3%
 
63335131.5%
 
52944827.8%
 
41565014.8%
 
351794.9%
 
29310.9%
 
11100.1%
 

V8
Real number (ℝ≥0)

ZEROS

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6082826604222533
Minimum0.0
Maximum10.0
Zeros9520
Zeros (%)9.0%
Memory size827.5 KiB
2020-08-25T01:52:40.364666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.765890718
Coefficient of variation (CV)0.6770319584
Kurtosis0.3191945302
Mean2.60828266
Median Absolute Deviation (MAD)1
Skewness0.7324272438
Sum276238
Variance3.118370027
2020-08-25T01:52:40.464440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
22615824.7%
 
12164020.4%
 
31995218.8%
 
41257811.9%
 
095209.0%
 
586058.1%
 
644344.2%
 
720181.9%
 
87470.7%
 
91770.2%
 
10790.1%
 
ValueCountFrequency (%) 
095209.0%
 
12164020.4%
 
22615824.7%
 
31995218.8%
 
41257811.9%
 
586058.1%
 
644344.2%
 
720181.9%
 
87470.7%
 
91770.2%
 
ValueCountFrequency (%) 
10790.1%
 
91770.2%
 
87470.7%
 
720181.9%
 
644344.2%
 
586058.1%
 
41257811.9%
 
31995218.8%
 
22615824.7%
 
12164020.4%
 

V9
Real number (ℝ≥0)

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.024577935566718
Minimum0.0
Maximum10.0
Zeros830
Zeros (%)0.8%
Memory size827.5 KiB
2020-08-25T01:52:40.575844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median5
Q36
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.881087861
Coefficient of variation (CV)0.374377288
Kurtosis0.02855207302
Mean5.024577936
Median Absolute Deviation (MAD)1
Skewness0.1166187292
Sum532143
Variance3.538491541
2020-08-25T01:52:40.675090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
52471623.3%
 
61900417.9%
 
41897917.9%
 
31288912.2%
 
71119810.6%
 
863206.0%
 
260995.8%
 
923682.2%
 
119501.8%
 
1015551.5%
 
08300.8%
 
ValueCountFrequency (%) 
08300.8%
 
119501.8%
 
260995.8%
 
31288912.2%
 
41897917.9%
 
52471623.3%
 
61900417.9%
 
71119810.6%
 
863206.0%
 
923682.2%
 
ValueCountFrequency (%) 
1015551.5%
 
923682.2%
 
863206.0%
 
71119810.6%
 
61900417.9%
 
52471623.3%
 
41897917.9%
 
31288912.2%
 
260995.8%
 
119501.8%
 

V10
Real number (ℝ≥0)

ZEROS

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.948313630698342
Minimum0.0
Maximum10.0
Zeros12144
Zeros (%)11.5%
Memory size827.5 KiB
2020-08-25T01:52:40.783484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.061111088
Coefficient of variation (CV)0.6186170313
Kurtosis-1.07763451
Mean4.948313631
Median Absolute Deviation (MAD)2
Skewness-0.07926101763
Sum524066
Variance9.370401095
2020-08-25T01:52:40.884805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
61303012.3%
 
01214411.5%
 
71211411.4%
 
51077210.2%
 
298649.3%
 
894588.9%
 
488548.4%
 
387368.2%
 
1083247.9%
 
967956.4%
 
158175.5%
 
ValueCountFrequency (%) 
01214411.5%
 
158175.5%
 
298649.3%
 
387368.2%
 
488548.4%
 
51077210.2%
 
61303012.3%
 
71211411.4%
 
894588.9%
 
967956.4%
 
ValueCountFrequency (%) 
1083247.9%
 
967956.4%
 
894588.9%
 
71211411.4%
 
61303012.3%
 
51077210.2%
 
488548.4%
 
387368.2%
 
298649.3%
 
158175.5%
 

V11
Real number (ℝ≥0)

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.874806435774445
Minimum0.0
Maximum10.0
Zeros1027
Zeros (%)1.0%
Memory size827.5 KiB
2020-08-25T01:52:40.999476image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.595990743
Coefficient of variation (CV)0.4418853235
Kurtosis-0.8496571744
Mean5.874806436
Median Absolute Deviation (MAD)2
Skewness0.02565655058
Sum622189
Variance6.739167935
2020-08-25T01:52:41.103726image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
51598715.1%
 
61512614.3%
 
101488914.1%
 
41239211.7%
 
71191911.3%
 
3103079.7%
 
885268.1%
 
268636.5%
 
959855.7%
 
128872.7%
 
010271.0%
 
ValueCountFrequency (%) 
010271.0%
 
128872.7%
 
268636.5%
 
3103079.7%
 
41239211.7%
 
51598715.1%
 
61512614.3%
 
71191911.3%
 
885268.1%
 
959855.7%
 
ValueCountFrequency (%) 
101488914.1%
 
959855.7%
 
885268.1%
 
71191911.3%
 
61512614.3%
 
51598715.1%
 
41239211.7%
 
3103079.7%
 
268636.5%
 
128872.7%
 

V12
Real number (ℝ≥0)

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.050241719228009
Minimum0.0
Maximum10.0
Zeros508
Zeros (%)0.5%
Memory size827.5 KiB
2020-08-25T01:52:41.217773image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q35
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.596247452
Coefficient of variation (CV)0.3941116513
Kurtosis-0.3796163217
Mean4.050241719
Median Absolute Deviation (MAD)1
Skewness0.1248314465
Sum428953
Variance2.548005928
2020-08-25T01:52:41.320429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
43029028.6%
 
31945318.4%
 
51637815.5%
 
61378913.0%
 
21354312.8%
 
761905.8%
 
145354.3%
 
811431.1%
 
05080.5%
 
9750.1%
 
104< 0.1%
 
ValueCountFrequency (%) 
05080.5%
 
145354.3%
 
21354312.8%
 
31945318.4%
 
43029028.6%
 
51637815.5%
 
61378913.0%
 
761905.8%
 
811431.1%
 
9750.1%
 
ValueCountFrequency (%) 
104< 0.1%
 
9750.1%
 
811431.1%
 
761905.8%
 
61378913.0%
 
51637815.5%
 
43029028.6%
 
31945318.4%
 
21354312.8%
 
145354.3%
 

target
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.952439853457718
Minimum0
Maximum5
Zeros21359
Zeros (%)20.2%
Memory size827.5 KiB
2020-08-25T01:52:41.431035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.417446185
Coefficient of variation (CV)0.7259871193
Kurtosis0.1886292561
Mean1.952439853
Median Absolute Deviation (MAD)1
Skewness0.6113606184
Sum206779
Variance2.009153687
2020-08-25T01:52:41.540662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
25269849.8%
 
02135920.2%
 
51196711.3%
 
31083210.2%
 
190528.5%
 
ValueCountFrequency (%) 
02135920.2%
 
190528.5%
 
25269849.8%
 
31083210.2%
 
51196711.3%
 
ValueCountFrequency (%) 
51196711.3%
 
31083210.2%
 
25269849.8%
 
190528.5%
 
02135920.2%
 

Interactions

2020-08-25T01:52:04.394995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:04.558896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:04.717933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:05.057936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:05.219089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:05.380101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:05.542506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:05.703426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:05.866234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:06.030353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:06.190007image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:06.350866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:06.515982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:06.676259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:06.840441image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:06.999534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:07.161725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:07.325536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:07.486711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:07.648890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:07.811934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:07.971227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:08.129430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:08.290145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:08.453391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:08.613309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:08.778919image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:08.945085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:09.105419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:09.266240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:09.431097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:09.594388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:09.752719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:09.913432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:10.072868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:10.230465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:10.584272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:10.750458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:10.914668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:11.078259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:11.247380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:11.412645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:11.575019image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:11.738034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:11.903229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:12.064855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:12.227989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:12.392329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:12.559636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:12.728824image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:12.892522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:13.057898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:13.220255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:13.386376image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:13.552078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:13.718387image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:13.889010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:14.062865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:14.226770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:14.386823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:14.551579image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:14.721293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:14.885847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:15.052321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:15.214594image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:15.375286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:15.543571image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:15.704225image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:15.862306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:16.210008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:16.369714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:16.530739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:16.690628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:16.854012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:17.018629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:17.191145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:17.352307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:17.524118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:17.689681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:17.851239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:18.011914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:18.172260image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:18.330270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:18.492136image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:18.654638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:18.824537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:18.993656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:19.155531image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:19.316468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:19.477252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:19.640660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:19.799416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:19.957103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:20.117271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:20.275717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:20.438515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:20.601814image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:20.767430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:20.929399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:21.091119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:21.249967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:21.406538image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:21.787964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:21.948642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:22.113832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:22.275602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:22.441481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:22.605123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:22.769548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:22.932325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:23.098325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:23.261028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:23.423507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:23.589927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:23.763881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:23.922765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:24.095018image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:24.260051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:24.420693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:24.580724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:24.745251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:24.906673image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:25.064615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:25.227889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:25.387847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:25.550315image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:25.709220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:25.869019image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:26.027377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:26.186204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:26.345062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:26.505903image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:26.669989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:26.828998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:26.989985image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:27.368078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:27.531833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:27.701752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:27.862955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:28.027537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:28.191384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:28.358415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:28.520303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:28.682472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:28.843229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:29.012851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:29.172244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:29.333566image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:29.497491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:29.661237image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:29.822869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:29.980381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:30.141698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:30.302683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:30.460567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:30.625509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:30.786090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:30.943323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:31.103551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:31.262745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:31.426791image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:31.589408image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:31.750715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:31.920357image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:32.080432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:32.238861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:32.403585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:32.562019image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:32.914399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:33.074025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:33.234364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:33.407358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:33.572006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:33.733342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:33.894217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:34.055590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:34.215339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:34.380858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:34.542439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:34.699679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:34.863532image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:35.030337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:35.191600image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:35.352589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:35.516690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:35.673551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:35.843592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:36.005940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:36.169509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:36.329008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:36.493275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:36.655255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:36.823102image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:36.986337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:37.149936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:37.314578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:52:41.864303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T01:52:42.115647image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T01:52:42.349929image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T01:52:42.588397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T01:52:37.624584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:52:38.018966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

V0V1V2V3V4V5V6V7V8V9V10V11V12target
00.02.02.00.08.03.07.05.01.03.07.05.05.01
10.05.09.00.06.00.07.06.01.07.02.09.03.00
20.05.07.00.06.01.06.07.05.05.05.06.05.02
30.00.00.010.07.00.04.07.03.06.04.06.04.05
45.06.08.00.08.06.04.05.08.03.04.07.04.00
50.05.04.00.09.04.04.05.02.07.04.07.06.01
60.06.07.00.03.00.07.07.02.06.06.06.03.02
70.09.010.00.08.00.06.05.01.08.01.08.03.01
80.05.07.00.06.01.06.06.02.07.02.08.04.02
90.00.00.00.06.01.06.06.04.05.04.05.05.02

Last rows

V0V1V2V3V4V5V6V7V8V9V10V11V12target
1058986.09.09.02.07.02.05.06.02.07.02.09.04.00
1058990.04.06.00.09.06.08.06.05.02.07.03.06.03
1059000.01.02.00.04.01.07.05.02.04.06.04.05.02
1059010.010.08.00.07.00.04.05.06.07.00.010.03.00
1059020.02.04.010.010.09.03.03.00.03.09.02.06.03
1059030.04.06.00.06.01.08.04.01.05.05.05.05.02
1059040.03.04.00.07.02.08.06.02.04.010.04.04.02
1059050.07.01.00.04.00.08.06.02.05.04.07.04.02
1059063.04.06.00.06.00.09.04.00.04.05.05.02.01
1059070.01.00.00.09.06.07.04.01.04.09.02.07.03

Duplicate rows

Most frequent

V0V1V2V3V4V5V6V7V8V9V10V11V12targetcount
24110.00.00.010.010.00.00.03.02.00.00.010.04.004
600.02.00.00.010.00.010.04.00.03.010.02.02.023
800.02.08.00.09.00.05.04.01.08.00.010.04.003
1220.04.07.00.04.00.05.06.04.07.02.010.03.023
1460.06.00.010.010.00.01.04.04.04.00.010.04.053
1610.06.09.00.07.00.04.04.04.08.00.010.04.003
2030.010.08.00.00.00.00.00.00.00.00.00.00.003
2040.010.08.00.00.00.00.00.00.00.01.00.00.003
2130.010.09.00.09.00.05.05.01.08.00.010.04.003
2150.010.010.00.00.00.00.01.00.010.02.00.01.003